Date of Award
11-2017
Access Type
Thesis - Open Access
Degree Name
Master of Science in Mechanical Engineering
Department
Mechanical Engineering
Committee Chair
Eric Coyle, Ph.D.
First Committee Member
Brian Butka, Ph.D.
Second Committee Member
Patrick Currier, Ph.D.
Abstract
Autonomous Surface Vehicles have the capability of replacing dull, dirty, and dangerous jobs in the maritime field. However, few successful ASV systems exist today, as there is a need for greater sensing capabilities. Furthermore, a successful ASV system requires object detection and recognition capabilities to enable autonomous navigation and situational awareness. This thesis demonstrates an application of LiDAR sensors in maritime environments for object detection, classification, and camera sensor fusion. This is accomplished through the integration of a high-fidelity GPS/INS system, 3D LiDAR sensors, and a pair of cameras. After rotating LiDAR returns into a global reference frame, they are reduced to a 3D occupancy grid. Objects are then extracted and classified with a Support Vector Machine (SVM) classifier. The LiDAR returns, when converted from a global frame to a camera frame, then allow the cameras to process a region of their imaging frame to assist in the classification of objects using color-based features. The SVM implementation results in an overall accuracy 98.7% for 6 classes. The transformation into pixel coordinates is shown here to be successful, with an angular error of 2 degrees, attributed to measurement error propagated through rotations.
Scholarly Commons Citation
Thompson, David John, "Maritime Object Detection, Tracking, and Classification Using Lidar and Vision-Based Sensor Fusion" (2017). Doctoral Dissertations and Master's Theses. 377.
https://commons.erau.edu/edt/377
